Development and evaluation of a chronic kidney disease risk prediction model using random forest.

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We sourced and merged gene expression profiles of CKD-affected renal tissues from the Gene Expression Omnibus (GEO) database, classifying them into two sets for training and validation in a 7:3 ratio. The training set included 141 CKD and 33 non-CKD specimens, while the validation set had 60 and 14, respectively. The disease risk prediction model was constructed using the training dataset, while the validation dataset confirmed the model's identification capabilities. The development of our predictive model began with evaluating differentially expressed genes (DEGs) between the two groups. We isolated six genes using Lasso and random forest (RF) methods-DUSP1, GADD45B, IFI44L, IFI30, ATF3, and LYZ-which are critical in differentiating CKD from non-CKD tissues. We refined our random forest (RF) model through 10-fold cross-validation, repeated five times, to optimize the mtry parameter. The performance of our model was robust, with an average AUC of 0.979 across the folds, translating to a 91.18% accuracy. Validation tests further confirmed its efficacy, with a 94.59% accuracy and an AUC of 0.990. External validation using dataset GSE180394 yielded an AUC of 0.913, 89.83% accuracy, and a sensitivity rate of 0.889, underscoring the model's reliability. In summary, the study identified critical genetic biomarkers and successfully developed a novel disease risk prediction model for CKD. This model can serve as a valuable tool for CKD disease risk assessment and contribute significantly to CKD identification.", "claims": [{"id": "claim-method", "statement": "We conducted differential expression analysis on the training dataset to identify Differentially Expressed Genes (DEGs) using conventional Bayesian approaches via the limma package.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38993480", "locator": "sections[7] \"Screening for DEGs\" paragraph 2", "quote": "We conducted differential expression analysis on the training dataset to identify Differentially Expressed Genes (DEGs) using conventional Bayesian approaches via the limma package."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding author.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38993480", "locator": "sections[23] \"Data availability statement\" paragraph 2", "quote": "The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding author."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The six-gene CKD prediction model achieved an external-validation AUC of 0.913, accuracy of 89.83%, and sensitivity of 0.889.", "kind": "quantitative", "target": {"value": 89.83, "unit": "per cent", "tolerance": null, "source": "External validation using dataset GSE180394 yielded an AUC of 0.913, 89.83% accuracy, and a sensitivity rate of 0.889, underscoring the model’s reliability."}, "evidence": [{"asset": "source:pmid:38993480", "locator": "sections[0] \"Abstract\" paragraph 1", "quote": "External validation using dataset GSE180394 yielded an AUC of 0.913, 89.83% accuracy, and a sensitivity rate of 0.889, underscoring the model’s reliability."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "The six-gene CKD prediction model achieved an external-validation AUC of 0.913, accuracy of 89.83%, and sensitivity of 0.889.", "steps": [{"id": "read_published_estimate", "order": 1, "name": "Read the published estimate from the source", "kind": "other", "state": "complete", "blocked_on": null, "description": "The claim, method and evidence span published with the source article.", "duration_hours": null, "parameters": {}, "inputs": ["source:pmid:38993480"], "outputs": ["the published estimate and its evidence"], "instruments": [], "reagents": []}, {"id": "obtain_source_data", "order": 2, "name": "Obtain the source data from the named repository", "kind": "data-preparation", "state": "complete", "blocked_on": null, "description": "The GEO series accession resolves and its sample accessions are listed. 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No dataset was downloaded and no licence was read."}}, "can_run": {"dsl_operations": ["normalise_counts", "differential_expression_fit", "fdr_bh"], "assets": ["source:pmid:38993480", "data:geo:GSE180394"], "steps": ["run_analysis"]}, "must_build": {"dsl_operations": [], "converters": [], "readers": [], "capabilities": [], "data_gaps": ["data:geo:GSE180394"]}, "gaps": []}, "review": {"state": "partial", "machine_critique": {"kind": "extraction_review", "reader": {"model": "MiniMax-M3.1-Flash", "provider": "minimax", "provider_family": "minimax", "read_at": "2026-11-01T19:23:46Z", "independence": "single_reader"}, "evidence_check": {"method": "verbatim", "availability": "verbatim"}, "calls": 5, "usage": {"prompt_tokens": 9693, "completion_tokens": 1672, "cached_tokens": 264}}, "human_critique": null, "stages": [{"id": "extraction", "state": "complete", "reader": "MiniMax-M3.1-Flash", "provider": "minimax"}], "gaps": ["review.independent_reader", "review.machine_critique", "review.human_critique"]}, "execution": {"state": "absent", "status": "not_run", "mode": null, "runs": [], "gaps": ["execution.not_run"]}, "provenance": {"entities": [{"id": "pmid:38993480", "type": "paper", "label": "pmid:38993480"}, {"id": "source:pmid:38993480", "type": "manuscript", "label": "PMID 38993480 full text"}, {"id": "data:geo:GSE180394", "type": "dataset", "label": "GEO GSE180394"}], "activities": [{"id": "activity-reading-sci-2026-11-01", "type": "classification", "used": [{"id": "pmid:38993480", "type": "paper", "label": "pmid:38993480"}, {"id": "source:pmid:38993480", "type": "manuscript", "label": "PMID 38993480 full text"}, {"id": "data:geo:GSE180394", "type": "dataset", "label": "GEO GSE180394"}], "agent": "agent:corpus-assay-engine"}, {"id": "activity-read-38993480", "type": "extraction", "used": ["source:pmid:38993480"], "agent": "agent:minimax-reader"}, {"id": "activity-resolve-GSE180394", "type": "accession_resolution", "used": ["data:geo:GSE180394"], "agent": "agent:corpus-assay-engine"}], "agents": [{"id": "agent:corpus-assay-engine", "type": "software", "label": "Corpus assay engine"}, {"id": "agent:minimax-reader", "type": "software", "label": "MiniMax literature reader"}], "source_run_id": "reading-sci-2026-11-01", "dsl_catalogue_fingerprint": "343fb30756ba6ec3", "capability_table_fingerprint": "2cb3e42f047d2ecf", "snapshot_id": "ba34b49490d9bfaf"}, "lineage": {"parent_id": "pmid:38993480", "parent_hash": "sha256:8c9069d147e4a5b92e3ad3dff98c439e15f2aa939726fac8792c1719b35af4ef", "fork_of": null, "generation": 3}, "gaps": ["asset:data:geo:GSE180394 licence not read", "asset:data:geo:GSE180394 not downloaded", "asset:data:geo:GSE32591 licence not read", "asset:data:geo:GSE32591 not downloaded", "asset:data:geo:GSE35488 licence not read", "asset:data:geo:GSE35488 not downloaded", "asset:data:geo:GSE47184 licence not read", "asset:data:geo:GSE47184 not downloaded", "asset:data:geo:GSE66494 licence not read", "asset:data:geo:GSE66494 not downloaded", "execution.not_run", "review.human_critique", "review.independent_reader", "review.machine_critique"], "assays": [{"role": "analysis", "definition_id": "differential_expression", "definition_version": "f2132e96ae6c7a4e", "definition_digest": null, "required_operations": ["normalise_counts", "differential_expression_fit", "fdr_bh"], "support_state": "supported", "source_hashes": {"dsl_catalogue": "f2132e96ae6c7a4e"}, "confidence": 1.0, "evidence": ["We conducted differential expression analysis on the training dataset to identify Differentially Expressed Genes (DEGs) using conventional Bayesian approaches via the limma package."], "order_basis": "analysis_only", "id": "assay:1"}]}